Results for “Complexity” — Page 9

NASA NTRS2014

Improved Algorithms for Accurate Retrieval of UV - Visible Diffuse Attenuation Coefficients in Optically Complex, Inshore Waters

Photochemical processes driven by high-energy ultraviolet radiation (UVR) in inshore, estuarine, and coastal waters play an important role in global bio geochemical cycles and biological systems. A key to modeling photochemical processes in these optically complex waters is an accurate description of the vertical distribution of UVR in the water column which can be obtained using the diffuse attenuation coefficients of down welling irradiance (Kd()). The Sea UV Sea UVc algorithms (Fichot et al., 2008) can accurately retrieve Kd ( 320, 340, 380,412, 443 and 490 nm) in oceanic and coastal waters using multispectral remote sensing reflectances (Rrs(), Sea WiFS bands). However, SeaUVSeaUVc algorithms are currently not optimized for use in optically complex, inshore waters, where they tend to severely underestimate Kd(). Here, a new training data set of optical properties collected in optically complex, inshore waters was used to re-parameterize the published SeaUVSeaUVc algorithms, resulting in improved Kd() retrievals for turbid, estuarine waters. Although the updated SeaUVSeaUVc algorithms perform best in optically complex waters, the published SeaUVSeaUVc models still perform well in most coastal and oceanic waters. Therefore, we propose a composite set of SeaUVSeaUVc algorithms, optimized for Kd() retrieval in almost all marine systems, ranging from oceanic to inshore waters. The composite algorithm set can retrieve Kd from ocean color with good accuracy across this wide range of water types (e.g., within 13 mean relative error for Kd(340)). A validation step using three independent, in situ data sets indicates that the composite SeaUVSeaUVc can generate accurate Kd values from 320 490 nm using satellite imagery on a global scale. Taking advantage of the inherent benefits of our statistical methods, we pooled the validation data with the training set, obtaining an optimized composite model for estimating Kd() in UV wavelengths for almost all marine waters. This optimized composite set of SeaUVSeaUVc algorithms will provide the optical community with improved ability to quantify the role of solar UV radiation in photochemical and photobiological processes in the ocean.

UV↗

NASA NTRS2016

Performance Analysis of a Hardware Implemented Complex Signal Kurtosis Radio-Frequency Interference Detector

Radio-frequency interference (RFI) is a known problem for passive remote sensing as evidenced in the L-band radiometers SMOS, Aquarius and more recently, SMAP. Various algorithms have been developed and implemented on SMAP to improve science measurements. This was achieved by the use of a digital microwave radiometer. RFI mitigation becomes more challenging for microwave radiometers operating at higher frequencies in shared allocations. At higher frequencies larger bandwidths are also desirable for lower measurement noise further adding to processing challenges. This work focuses on finding improved RFI mitigation techniques that will be effective at additional frequencies and at higher bandwidths. To aid the development and testing of applicable detection and mitigation techniques, a wide-band RFI algorithm testing environment has been developed using the Reconfigurable Open Architecture Computing Hardware System (ROACH) built by the Collaboration for Astronomy Signal Processing and Electronics Research (CASPER) Group. The testing environment also consists of various test equipment used to reproduce typical signals that a radiometer may see including those with and without RFI. The testing environment permits quick evaluations of RFI mitigation algorithms as well as show that they are implementable in hardware. The algorithm implemented is a complex signal kurtosis detector which was modeled and simulated. The complex signal kurtosis detector showed improved performance over the real kurtosis detector under certain conditions. The real kurtosis is implemented on SMAP at 24 MHz bandwidth. The complex signal kurtosis algorithm was then implemented in hardware at 200 MHz bandwidth using the ROACH. In this work, performance of the complex signal kurtosis and the real signal kurtosis are compared. Performance evaluations and comparisons in both simulation as well as experimental hardware implementations were done with the use of receiver operating characteristic (ROC) curves.

radiometers↗

NASA NTRS2017

GRAIL Gravity Observations of the Transition from Complex Crater to Peak-Ring Basin on the Moon: Implications for Crustal Structure and Impact Basin Formation

High-resolution gravity data from the Gravity Recovery and Interior Laboratory (GRAIL) mission provide the opportunity to analyze the detailed gravity and crustal structure of impact features in the morphological transition from complex craters to peak-ring basins on the Moon. We calculate average radial profiles for free-air anomalies and Bouguer anomalies for peak-ring basins, proto-basins, and the largest complex craters. Complex craters and proto-basins have free-air anomalies that are positively correlated with surface topography, unlike the prominent lunar mascons (positive free-air anomalies in areas of low elevation) associated with large basins. The Bouguer gravity anomaly profiles of complex craters are highly irregular, with central positive anomalies that are generally absent or not clearly tied to interior morphology. In contrast, gravity profiles for peak-ring basins (approx. 200 km to 580 km) are much more regular and are highly correlated with surface morphology. A central positive Bouguer anomaly is confined within the peak ring and a negative Bouguer anomaly annulus extends from the edge of the positive anomaly outward to about the rim crest. A number of degraded basins lacking interior peak rings have diameters and gravity patterns similar to those of well-preserved peak-ring basins. If these structures represent degraded peak-ring basins, the number of peak-ring basins on the Moon would increase by more than a factor of two to 34. The gravity anomalies within basins are interpreted to be due to uplift of the mantle confined within the peak ring and an annulus of thickened crust between the peak ring and rim crest. We hypothesize that mantle uplift is influenced by interaction between the transient cavity and the mantle. Further, mascon formation is generally disconnected from the number of basin rings formed and occurs over a wide range of basin sizes. These observations have important implications for models of basin and mascon formation on the Moon and other planetary bodies.

Baker, David M. H.↗

NASA NTRS2017

Manned Versus Unmanned Risk and Complexity Considerations for Future Midsized X-Planes

The objective of this work was to identify and estimate complexity and risks associated with the development and testing of new low-cost medium-scale X-plane aircraft primarily focused on air transport operations. Piloting modes that were evaluated for this task were manned, remotely piloted, and unmanned flight research programs. This analysis was conducted early in the data collection period for X-plane concept vehicles before preliminary designs were complete. Over 50 different aircraft and system topics were used to evaluate the three piloting control modes. Expert group evaluations from a diverse set of pilots, engineers, and other experts at Aeronautics Research Mission Directorate centers within the National Aeronautics and Space Administration provided qualitative reasoning on the many issues surrounding the decisions regarding piloting modes. The group evaluations were numerically rated to evaluate each topic quantitatively and were used to provide independent criteria for vehicle complexity and risk. An Edwards Air Force Base instruction document was identified that emerged as a source of the effects found in our qualitative and quantitative data. The study showed that a manned aircraft was the best choice to align with test activities for transport aircraft flight research from a low-complexity and low-risk perspective. The study concluded that a manned aircraft option would minimize the risk and complexity to improve flight-test efficiency and bound the cost of the flight-test portion of the program. Several key findings and discriminators between the three modes are discussed in detail.

x-planes↗

NASA NTRS2017

Manned Versus Unmanned Risk and Complexity Considerations for Future Midsized X-Planes

The objective of this work was to identify and estimate complexity and risks associated with the development and testing of new low-cost medium-scale X-plane aircraft primarily focused on air transport operations. Piloting modes that were evaluated for this task were manned, remotely piloted, and unmanned flight research programs. This analysis was conducted early in the data collection period for X-plane concept vehicles before preliminary designs were complete. Over 50 different aircraft and system topics were used to evaluate the three piloting control modes. Expert group evaluations from a diverse set of pilots, engineers, and other experts at Aeronautics Research Mission Directorate centers within the National Aeronautics and Space Administration provided qualitative reasoning on the many issues surrounding the decisions regarding piloting modes. The group evaluations were numerically rated to evaluate each topic quantitatively and were used to provide independent criteria for vehicle complexity and risk. An Edwards Air Force Base instruction document was identified that emerged as a source of the effects found in our qualitative and quantitative data. The study showed that a manned aircraft was the best choice to align with test activities for transport aircraft flight research from a low-complexity and low-risk perspective. The study concluded that a manned aircraft option would minimize the risk and complexity to improve flight-test efficiency and bound the cost of the flight-test portion of the program. Several key findings and discriminators between the three modes are discussed in detail.

quanitive analysis↗

NASA NTRS2018

Numerical Simulation of AFP Nip Point Temperature Prediction for Complex Geometries

Material placement at the ideal nip point temperature over complex surfaces with uniformity across the width of the compaction rollers results in optimized part properties for Automated Fiber Placement (AFP) processes. However, current AFP systems utilize heat control models and methodologies, based on multiple process parameters such as feed-rate and orientation, that are mostly open-loop. Here, infrared (IR) heater input is calibrated as a function of process parameters during machine qualification. This work presents a numerical simulation to predict arrayed-infrared (AIR) emitter radiation onto a substrate that includes view factor implementation, IR radiative heat flow calculation, energy rate balance, and a transient heat transfer model. The purpose of this numerical model is to predict nip point temperature on complex surfaces, serving as a baseline for a new arrayed-infrared (AIR) thermoset heater to improve AFP process control. It is anticipated that this simulation will accurately control the temperature for high-speed AFP layup of complex geometries. An anticipated result of an AIR heater system is that material calibration and testing will be reduced as temperature is instantaneously monitored and controlled. Therefore, temperature across the roller width will be uniform during placement of complex parts, independent of their geometry.

Xia, Kaishu↗

NASA NTRS2018

Complexity-Based Link Assignment for NASA’s Deep Space Network for Follow-the-Sun Operations

NASA’s Deep Space Network (DSN) recently underwent a paradigm shift in its operations approach called Follow the Sun Operations (FtSO) in an effort to increase efficiency for forthcoming expansion of the network. This change requires each Deep Space Communications Complex (DSCC) to remotely control the other two complexes’ antennas during their local day shift, in contrast to locally controlling only their own antennas 24x7. Remote operations increases the workload of each complex during their day shift, specifically that of the Link Control Operators (LCOs), and presents a new challenge for planning and managing the distribution of responsibility for each link. A new DSN software assembly, the Link Complexity and Maintenance (LCM) software, was developed to support workload management for LCOs, as well as for planning site-local maintenance activities. The LCM deployment was a vital part of the transition to FtSO in November 2017. This paper discusses the architecture of LCM, its feature set, and lessons learned during its development and rollout.

Lee, Carlyn↗

NASA NTRSDate not supplied

Augmenting the Space Environment Complex's Thermal Vacuum Capabilities for Artemis 1 Orion Spacecraft Testing

The Space Environments Complex at Glenn Research Center’s Neil A Armstrong Test Facility (formerly Plum Brook Station) recently successfully completed Artemis 1 thermal vacuum testing. The Space Environments Complex boasts the world’s largest thermal vacuum chamber with a 122-foot tall, 100-foot diameter aluminum assembly vacuum chamber. The fifty-year old chamber has been adapted over its history to conduct several unique test campaigns in a variety of test environments. Artemis 1 Orion spacecraft testing is the most complex test campaign the Space Environments Complex has undergone to date, with temperature limits between -250 F to +300F in a high vacuum with significant requirements for quality data and contamination control of a flight vehicle. This presentation covers the improvements and innovations in the cryoshroud and thermal systems, data acquisition, as well as contamination and cleanliness to accommodate the rigors of a flight vehicle during a six-week thermal vacuum test. Additionally, the presentation covers successes and lessons learned from the testing.

Erin Reed↗

NASA NTRSDate not supplied

Physics-Guided Deep Learning for Complex System Health Management and Decision Making

The landscape of complex engineered systems is rapidly evolving, from smart manufacturing facilities to next-generation transportation vehicles. As these systems become increasingly sophisticated and interconnected, the need for advanced health management systems grows ever more critical. These systems must go beyond simple monitoring, actively predicting potential failures before they occur. This paradigm shift from fixed maintenance schedules to condition-based predictions is key to optimizing system performance, enhancing safety, and paving the way for autonomous decision-making across various industries. Whether in industrial processes, energy systems, or advanced transportation, the ability to anticipate and prevent failures is becoming a cornerstone of operational excellence. To accurately predict the future health of any complex system, knowledge of its current health state and future operational conditions is essential. Recent advancements in data-driven algorithms have generated growing interest in artificial intelligence for industrial applications. However, the limitations of pure data-driven methods, particularly in industries where data acquisition is costly and limited, have become apparent. This has led to a focus on blending physics with data-driven algorithms, mitigating the drawbacks of both approaches while emphasizing their respective advantages. This research proposes a novel framework for integrating physics-based performance models with deep learning algorithms for the prognostics of complex safety-critical systems. In this approach, physics-based models serve as a blueprint, capturing fundamental system behaviors, while deep learning algorithms, leveraging real-world sensor data, fill in gaps and identify subtle patterns indicative of potential problems. This hybrid methodology, utilizing techniques such as Physics-Informed Neural Networks (PINNs), offers a powerful solution for predicting system health. By fusing domain knowledge with data-driven insights, this approach promises more accurate, adaptable, and reliable models for health prediction. The resulting framework is versatile, applicable across various sectors including aerospace, manufacturing, and energy systems, ultimately contributing to safer, more efficient operations in our increasingly complex technological landscape.

Diagnostics↗

NASA NTRS2014

Complexity and Pilot Workload Metrics for the Evaluation of Adaptive Flight Controls on a Full Scale Piloted Aircraft

Flight research has shown the effectiveness of adaptive flight controls for improving aircraft safety and performance in the presence of uncertainties. The National Aeronautics and Space Administration's (NASA)'s Integrated Resilient Aircraft Control (IRAC) project designed and conducted a series of flight experiments to study the impact of variations in adaptive controller design complexity on performance and handling qualities. A novel complexity metric was devised to compare the degrees of simplicity achieved in three variations of a model reference adaptive controller (MRAC) for NASA's F-18 (McDonnell Douglas, now The Boeing Company, Chicago, Illinois) Full-Scale Advanced Systems Testbed (Gen-2A) aircraft. The complexity measures of these controllers are also compared to that of an earlier MRAC design for NASA's Intelligent Flight Control System (IFCS) project and flown on a highly modified F-15 aircraft (McDonnell Douglas, now The Boeing Company, Chicago, Illinois). Pilot comments during the IRAC research flights pointed to the importance of workload on handling qualities ratings for failure and damage scenarios. Modifications to existing pilot aggressiveness and duty cycle metrics are presented and applied to the IRAC controllers. Finally, while adaptive controllers may alleviate the effects of failures or damage on an aircraft's handling qualities, they also have the potential to introduce annoying changes to the flight dynamics or to the operation of aircraft systems. A nuisance rating scale is presented for the categorization of nuisance side-effects of adaptive controllers.

pilot workload metrics↗

NASA NTRS2019

Complex Organic Molecules in Star-Forming Regions of the Magellanic Clouds

The Large and Small Magellanic Clouds (LMC and SMC), gas-rich dwarf companions of the Milky Way, are the nearest laboratories for detailed studies on the formation and survival of complex organic molecules (COMs) under metal poor conditions. To date, only methanol, methyl formate, and dimethyl ether have been detected in these galaxies – all three toward two hot cores in the N113 star-forming region in the LMC, the only extragalactic sources exhibiting complex hot core chemistry. We describe a small and diverse sample of the LMC and SMC sources associated with COMs or hot core chemistry, and compare the observations to theoretical model predictions. Theoretical models accounting for the physical conditions and metallicity of hot molecular cores in the Magellanic Clouds have been able to broadly account for the existing observations, but fail to reproduce the dimethyl ether abundance by more than an order of magnitude. We discuss future prospects for research in the field of complex chemistry in the low-metallicity environment. The detection of COMs in the Magellanic Clouds has important implications for astrobiology. The metallicity of the Magellanic Clouds is similar to galaxies in the earlier epochs of the Universe, thus the presence of COMs in the LMC and SMC indicates that a similar prebiotic chemistry leading to the emergence of life, as it happened on Earth, is possible in low-metallicity systems in the earlier Universe.

Magellanic Clouds, star formation, astrochemistry,↗

NASA NTRS2021

Reduced Complexity Model Intercomparison Project Phase 2: Synthesizing Earth System Knowledge for Probabilistic Climate Projections

Over the last decades, climate science has evolved rapidly across multiple expert domains. Our best tools to capture state-of-the-art knowledge in an internally self-consistent modelling framework are the increasingly complex fully coupled Earth System Models (ESMs). However, computational limitations and the structural rigidity of ESMs mean that the full range of uncertainties across multiple domains are difficult to capture with ESMs alone. The tools of choice are instead more computationally efficient reduced complexity models (RCMs), which are structurally flexible and can span the response dynamics across a range of domain-specific models and ESM experiments. Here we present Phase 2 of the Reduced Complexity Model Intercomparison Project (RCMIP Phase 2), the first comprehensive intercomparison of RCMs that are probabilistically calibrated with key benchmark ranges from specialized research communities. Unsurprisingly, but crucially, we find that models which have been constrained to reflect the key benchmarks better reflect the key benchmarks. Under the low-emissions SSP1-1.9 scenario, across the RCMs, median peak warming projections range from 1.3 to 1.7°C (relative to 1850-1900, using an observationally-based historical warming estimate of 0.8°C between 1850-1900 and 1995-2014). Further developing methodologies to constrain these projection uncertainties seems paramount given the international community's goal to contain warming to below 1.5°C above pre-industrial in the long-term. Our findings suggest that users of RCMs should carefully evaluate their RCM, specifically its skill against key benchmarks and consider the need to include projections benchmarks either from ESM results or other assessments to reduce divergence in future projections.

Climate↗

NASA NTRSDate not supplied

Detecting and Characterizing Patterns of Failure in Complex Engineered Systems: an Ontology Development and Clustering Approach

While the causes of failures in complex engineered systems are often clear in hindsight, it can be challenging to predict failures proactively during the design of novel engineered products or systems. Identifying patterns can be useful for capturing common characteristics that may lead to failure. In this paper, we present a methodology for identifying patterns of failure from NASA’s publicly available Lessons Learned Information System (LLIS). We apply an ontology development and clustering approach to identify representative patterns leading to failures in historical lessons learned. A joint inductive-deductive approach reveals the key themes in lessons that lead to failure, which are formalized and recorded as an ontology of complex systems failure causes. Documents from the LLIS are manually tagged with relevant characteristics from the ontology. From the tagged set, clustering is used to capture co-occurring sets of characteristics that lead to failure. The primary contribution of this work is a method for extracting a set of generic failure patterns in complex engineered systems and characteristics for these patterns that can be identified at design time, knowledge of which can be used to plan mitigation strategies.

Systems Engineering↗

NASA NTRS2023

Study of Pairwise Deconfliction Metrics to Analyze Air Traffic Complexity in Upper Class E Airspace

Upper Class E Traffic Management (ETM) is envisioned to cooperatively facilitate operations of a diverse set of aerial vehicles, such as high-altitude long-endurance fixed-wing unmanned aircraft (low-speed and high-speed), high-altitude platforms, airships, stratospheric balloons, supersonic unmanned and commercial aircraft, etc., with a wide variety of mission types, performance characteristics, communication, navigation and surveillance capabilities, maneuverability, and on-board avionics in the National Airspace System (NAS) ’above’ 60,000 feet above mean sea level, without an active and direct control from human air traffic controllers. A diverse mixture of aerial vehicle types creates significant challenges in understanding air traffic complexity, which may not correlate strongly with air traffic density. One key step for determining air traffic complexity in upper class E airspace is to first understand pairwise deconfliction metrics such as reachability, reserve area, and reserve flight time for each pair of unique aerial vehicle types under potential conflict. Therefore, pairwise deconfliction metrics are first defined, and analytical equations are derived for conflict resolution using the heading change maneuver. Next, case studies are performed to analyze deconfliction metrics to avoid secondary conflicts in upper class E airspace. The study shows that pairwise deconfliction metrics are functions of maneuverability, performance characteristics, uncertainty in position and velocity, heading angle change, and conflict angle of aerial vehicles. The next step for this research is to build a mathematical model for air traffic complexity using pairwise deconfliction metrics and validate it in an upper Class E simulation environment.

Airspace Complexity↗

NASA NTRSDate not supplied

Study of Pairwise Deconfliction Metrics to Analyze Air Traffic Complexity in Upper Class E Airspace

Upper Class E Traffic Management (ETM) is envisioned to cooperatively facilitate operations of a diverse set of aerial vehicles, such as high-altitude long-endurance fixed-wing unmanned aircraft (low-speed and high-speed), high-altitude platforms, airships, stratospheric balloons, supersonic unmanned and commercial aircraft, etc., with a wide variety of mission types, performance characteristics, communication, navigation and surveillance capabilities, maneuverability, and on-board avionics in the National Airspace System (NAS) ’above’ 60,000 feet above mean sea level, without an active and direct control from human air traffic controllers. A diverse mixture of aerial vehicle types creates significant challenges in understanding air traffic complexity, which may not correlate strongly with air traffic density. One key step for determining air traffic complexity in upper class E airspace is to first understand pairwise deconfliction metrics such as reachability, reserve area, and reserve flight time for each pair of unique aerial vehicle types under potential conflict. Therefore, pairwise deconfliction metrics are first defined, and analytical equations are derived for conflict resolution using the heading change maneuver. Next, case studies are performed to analyze deconfliction metrics to avoid secondary conflicts in upper class E airspace. The study shows that pairwise deconfliction metrics are functions of maneuverability, performance characteristics, uncertainty in position and velocity, heading angle change, and conflict angle of aerial vehicles. The next step for this research is to build a mathematical model for air traffic complexity using pairwise deconfliction metrics and validate it in an upper Class E simulation environment.

Airspace Complexity↗

NASA NTRSDate not supplied

Detecting and Characterizing Patterns of Failure in Complex Systems: An Ontology Development and Clustering Approach

While the causes of failures in complex engineered systems are often clear in hindsight, it can be challenging to predict failures proactively during the design of novel engineered products or systems. Identifying patterns can be useful for capturing common characteristics that may lead to failure. In this paper, we present a methodology for identifying patterns of failure from NASA’s publicly available Lessons Learned Information System (LLIS). We apply an ontology development and clustering approach to identify representative patterns leading to failures in historical lessons learned. A joint inductive-deductive approach reveals the key themes in lessons that lead to failure, which are formalized and recorded as an ontology of complex systems failure causes. Documents from the LLIS are manually tagged with relevant characteristics from the ontology. From the tagged set, clustering is used to capture co-occurring sets of characteristics that lead to failure. The primary contribution of this work is a method for extracting a set of generic failure patterns in complex engineered systems and characteristics for these patterns that can be identified at design time, knowledge of which can be used to plan mitigation strategies.

Systems Engineering↗

NASA NTRS1972

A parametric study of the complexity of sequential decoders, volume 1

The complexity and performance of practical sequential decoders were studied. Upon comparing sequential decoders with Viterbi decoders, it was found that sequential decoders outperform Viterbi decoders at low data rates (19.2 to 76.8 kbps) for the same complexity. However, Viterbi decoders are less complex than sequential decoders at high data rates (9.1 Mbps) for the same performance.

Huth, G. K.↗

NASA NTRS1972

Fourier transform C-13 NMR analysis of some free and potassium-ion complexed antibiotics.

Fourier transforms of the noise-decoupled, natural abundance C-13 NMR free induction decays of the cyclic antibiotic valinomycin and its potassium-ion complex have been obtained at 25.2 MHz. Comparisons are made with C-13 NMR spectra taken at 22.6 MHz of the cyclic antibiotic nonactin and the synthetic polyether dicyclohexyl-18-crown-6 and their potassium complexes. The results obtained suggest that conformational rearrangements of the molecule as a whole can compete with direct interactions between carbons and the potassium ion in determining C-13 chemical shift differences between the free and complexed species.

Ohnishi, M.↗